{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                          | 0/400 [00:00<?, ?it/s]C:\\Users\\wangyifan\\AppData\\Local\\Temp\\ipykernel_6592\\2945620132.py:46: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  x_layer = F.softmax(x_layer)\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 400/400 [00:22<00:00, 18.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time is 0:00:22.150418 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000 0.57799643\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1、导入包和定义参数\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as tud\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import trange\n",
    "EPOCHES = 400\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "\n",
    "#2、定义数据，这里是自己建立\n",
    "torch.manual_seed(1)\n",
    "n_data = torch.ones(100, 2)     #内容为一个 100 行 2 列 的 tensor\n",
    "x0 = torch.normal(3*n_data, 1)  \n",
    "y0 = torch.zeros(100)  \n",
    "x1 = torch.normal(-2*n_data, 1)  \n",
    "y1 = torch.ones(100)\n",
    "x2 = torch.normal(10*n_data, 1)\n",
    "y2 = torch.ones(100)*2\n",
    "x = torch.cat((x0, x1,x2)).type(torch.FloatTensor)  \n",
    "y = torch.cat((y0, y1,y2)).type(torch.LongTensor) \n",
    "\n",
    "torch_dataset = tud.TensorDataset(x,y)\n",
    "loader = tud.DataLoader(\n",
    "    dataset=torch_dataset,\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "if USE_CUDA:\n",
    "    x = x.cuda()\n",
    "    y = y.cuda()\n",
    "    \n",
    "#3、定义网络和模型\n",
    "#两层的网络\n",
    "class Net(torch.nn.Module):  \n",
    "    def __init__(self, n_feature, n_hidden, n_output):  \n",
    "        super(Net, self).__init__()  \n",
    "        self.n_hidden = nn.Linear(n_feature, n_hidden) \n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "        self.out = nn.Linear(n_hidden, n_output)  \n",
    "    def forward(self, x_layer):  \n",
    "        x_layer = torch.relu(self.n_hidden(x_layer))  \n",
    "        x_layer = self.dropout(x_layer)\n",
    "        x_layer = self.out(x_layer) \n",
    "        x_layer = F.softmax(x_layer)  \n",
    "        return x_layer  \n",
    "\n",
    "model = Net(n_feature=2, n_hidden=20, n_output=3)  \n",
    "if USE_CUDA:\n",
    "    model = model.cuda()\n",
    "# print(net)  \n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.02)  \n",
    "loss_func =  nn.CrossEntropyLoss()\n",
    "\n",
    "# 4、 完成训练和迭代\n",
    "from datetime import  datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "losses = []\n",
    "for i in trange(EPOCHES):\n",
    "    for step,(batch_x,batch_y) in enumerate(loader):\n",
    "        x_b = batch_x\n",
    "        y_b = batch_y\n",
    "        if USE_CUDA:\n",
    "            x_b = x_b.cuda()\n",
    "            y_b = y_b.cuda()\n",
    "        out = model(x_b)  \n",
    "      # print(out.shape, y.shape)  \n",
    "        loss = loss_func(out, y_b) \n",
    "        #losses.append(loss)\n",
    "        losses.append(loss.cpu().detach().numpy())   # 新版本需求\n",
    "        optimizer.zero_grad()  \n",
    "        loss.backward()  \n",
    "        optimizer.step() \n",
    "\n",
    "end_time = datetime.now()       \n",
    "msecs = (end_time - start_time) \n",
    "print(f\"time is {msecs} s\" )\n",
    "\n",
    "#5、其它--打印损失\n",
    "x1 = range(0,len(losses))\n",
    "plt.plot(x1,losses,\".-\")\n",
    "plt.xlabel(\"epoches\")\n",
    "plt.ylabel(\"test loss\")\n",
    "plt.show()\n",
    "\n",
    "print(len(losses),losses[-1])\n",
    "\n",
    "#5、其它--结果打印 \n",
    "train_result = model(x)  \n",
    "# print(train_result.shape)  \n",
    "train_predict = torch.max(train_result, 1)[1]  \n",
    "x_  = x.data.cpu().numpy()\n",
    "plt.scatter(x_[:, 0],x_[:, 1], c=train_predict.cpu().data.numpy(), s=100, lw=0, cmap='RdYlGn')  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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